We present a new ranking algorithm that combines the strengths of two previous
methods: boosted tree classification, and LambdaRank, which has been shown to be
empirically optimal for a widely used information retrieval measure. The
algorithm is based on boosted regression trees, although the ideas apply to any
weak learners, and it is significantly faster in both train and test phases than
the state of the art, for comparable accuracy. We also show how to find the
optimal linear combination for any two rankers, and we use this method to solve
the line search problem exactly during boosting. In addition, we show that
starting with a previously trained model, and boosting using its residuals,
furnishes an effective technique for model adaptation, and we give results for a
particularly pressing problem in Web Search - training rankers for markets for
which only small amounts of labeled data are available, given a ranker trained on
much more data from a larger market.